db.collection.updateMany()

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Definition

Compatibility
Syntax
Access Control
Behavior
Examples

MongoDB with drivers

This page documents a mongosh method. To see the equivalent method in a MongoDB driver, see the corresponding page for your programming language:

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Definition

db.collection.updateMany(filter, update, options): Updates all documents that match the specified filter for a collection.

Compatibility

This method is available in deployments hosted in the following environments:

MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud

Note

This command is supported in all MongoDB Atlas clusters. For information on Atlas support for all commands, see Unsupported Commands.

MongoDB Enterprise: The subscription-based, self-managed version of MongoDB
MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB

Syntax

The updateMany() method has the following form:

db.collection.updateMany(
   <filter>,
   <update>,
   {
     upsert: <boolean>,
     writeConcern: <document>,
     collation: <document>,
     arrayFilters: [ <filterdocument1>, ... ],
     hint:  <document|string>,
     let: <document>
   }
)

Parameters

The updateMany() method takes the following parameters:

Parameter

Type

Description

filter

document

The selection criteria for the update. The same query selectors as in the find() method are available.

Specify an empty document { } to update all documents in the collection.

update

document or pipeline

The modifications to apply. Can be one of the following:

Update document	Contains only update operator expressions. For more information, see Update with an Update Operator Expressions Document
Aggregation pipeline	Contains only the following aggregation stages: `$addFields` and its alias `$set` `$project` and its alias `$unset` `$replaceRoot` and its alias `$replaceWith`. For more information, see Update with an Aggregation Pipeline.

To update with a replacement document, see db.collection.replaceOne().

upsert

boolean

Optional. When true, updateMany() either:

Creates a new document if no documents match the filter. For more details see upsert behavior.
Updates documents that match the filter.

To avoid multiple upserts, ensure that the filter fields are uniquely indexed.

Defaults to false.

writeConcern

document

Optional. A document expressing the write concern. Omit to use the default write concern.

Do not explicitly set the write concern for the operation if run in a transaction. To use write concern with transactions, see Transactions and Write Concern.

collation

document

Optional.

Specifies the collation to use for the operation.

Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks.

The collation option has the following syntax:

collation: {
   locale: <string>,
   caseLevel: <boolean>,
   caseFirst: <string>,
   strength: <int>,
   numericOrdering: <boolean>,
   alternate: <string>,
   maxVariable: <string>,
   backwards: <boolean>
}

When specifying collation, the locale field is mandatory; all other collation fields are optional. For descriptions of the fields, see Collation Document.

If the collation is unspecified but the collection has a default collation (see db.createCollection()), the operation uses the collation specified for the collection.

If no collation is specified for the collection or for the operations, MongoDB uses the simple binary comparison used in prior versions for string comparisons.

You cannot specify multiple collations for an operation. For example, you cannot specify different collations per field, or if performing a find with a sort, you cannot use one collation for the find and another for the sort.

arrayFilters

array

Optional. An array of filter documents that determine which array elements to modify for an update operation on an array field.

In the update document, use the $[<identifier>] filtered positional operator to define an identifier, which you then reference in the array filter documents. You cannot have an array filter document for an identifier if the identifier is not included in the update document.

The <identifier> must begin with a lowercase letter and contain only alphanumeric characters.

You can include the same identifier multiple times in the update document; however, for each distinct identifier ($[identifier]) in the update document, you must specify exactly one corresponding array filter document. That is, you cannot specify multiple array filter documents for the same identifier. For example, if the update statement includes the identifier x (possibly multiple times), you cannot specify the following for arrayFilters that includes 2 separate filter documents for x:

// INVALID
[
  { "x.a": { $gt: 85 } },
  { "x.b": { $gt: 80 } }
]

However, you can specify compound conditions on the same identifier in a single filter document, such as in the following examples:

// Example 1
[
  { $or: [{"x.a": {$gt: 85}}, {"x.b": {$gt: 80}}] }
]
// Example 2
[
  { $and: [{"x.a": {$gt: 85}}, {"x.b": {$gt: 80}}] }
]
// Example 3
[
  { "x.a": { $gt: 85 }, "x.b": { $gt: 80 } }
]

For examples, see Specify arrayFilters for an Array Update Operations.

hint

Document or string

Optional. A document or string that specifies the index to use to support the query predicate.

The option can take an index specification document or the index name string.

If you specify an index that does not exist, the operation errors.

For an example, see Specify hint for Update Operations.

let

Document

Optional.

Specifies a document with a list of variables. This allows you to improve command readability by separating the variables from the query text.

The document syntax is:

{
  <variable_name_1>: <expression_1>,
  ...,
  <variable_name_n>: <expression_n>
}

The variable is set to the value returned by the expression, and cannot be changed afterwards.

To access the value of a variable in the command, use the double dollar sign prefix ($$) together with your variable name in the form $$<variable_name>. For example: $$targetTotal.

To use a variable to filter results, you must access the variable within the $expr operator.

For a complete example using let and variables, see Update with let Variables.

Returns

The method returns a document that contains:

A boolean acknowledged as true if the operation ran with write concern or false if write concern was disabled
matchedCount containing the number of matched documents
modifiedCount containing the number of modified documents
upsertedId containing the _id for the upserted document
upsertedCount containing the number of upserted documents

Access Control

On deployments running with authorization, the user must have access that includes the following privileges:

update action on the specified collection(s).
find action on the specified collection(s).
insert action on the specified collection(s) if the operation results in an upsert.

The built-in role readWrite provides the required privileges.

Behavior

updateMany() updates all matching documents in the collection that match the filter, using the update criteria to apply modifications.

Upsert

If upsert: true and no documents match the filter, db.collection.updateMany() creates a new document based on the filter and update parameters.

If you specify upsert: true on a sharded collection, you must include the full shard key in the filter. For additional db.collection.updateMany() behavior, see Sharded Collections.

See Update Multiple Documents with Upsert.

Update with an Update Operator Expressions Document

For the modification specification, the db.collection.updateMany() method can accept a document that only contains update operator expressions to perform.

For example:

db.collection.updateMany(
   <query>,
   { $set: { status: "D" }, $inc: { quantity: 2 } },
   ...
)

Update with an Aggregation Pipeline

The db.collection.updateMany() method can accept an aggregation pipeline [ <stage1>, <stage2>, ... ] that specifies the modifications to perform. The pipeline can consist of the following stages:

$addFields and its alias $set
$project and its alias $unset
$replaceRoot and its alias $replaceWith.

Using the aggregation pipeline allows for a more expressive update statement, such as expressing conditional updates based on current field values or updating one field using the value of another field(s).

For example:

db.collection.updateMany(
   <query>,
   [
      { $set: { status: "Modified", comments: [ "$misc1", "$misc2" ] } },
      { $unset: [ "misc1", "misc2" ] }
   ]
   ...
)

Note

In this pipeline, $set and $unset are aggregation stages, as opposed to update operators. The aggregation stages $set and $unset add new fields to documents and do not modify existing field values.

For more information on the update operators, see $set and $unset.

For examples, see Update with Aggregation Pipeline.

Capped Collections

If an update operation changes the document size, the operation will fail.

Time Series Collections

The updateMany() method is available for time series collections starting in MongoDB 5.1.

Update commands must meet the following requirements:

You can only match on the metaField field value.
You can only modify the metaField field value.
Your update document can only contain update operator expressions.
Your update command must not limit the number of documents to be updated. Set multi: true or use the updateMany() method.
Your update command must not set upsert: true.

Sharded Collections

For a db.collection.updateMany() operation that includes upsert: true and is on a sharded collection, you must include the full shard key in the filter.

Explainability

updateMany() is not compatible with db.collection.explain().

Transactions

db.collection.updateMany() can be used inside distributed transactions.

Important

In most cases, a distributed transaction incurs a greater performance cost over single document writes, and the availability of distributed transactions should not be a replacement for effective schema design. For many scenarios, the denormalized data model (embedded documents and arrays) will continue to be optimal for your data and use cases. That is, for many scenarios, modeling your data appropriately will minimize the need for distributed transactions.

For additional transactions usage considerations (such as runtime limit and oplog size limit), see also Production Considerations.

Upsert within Transactions

You can create collections and indexes inside a distributed transaction if the transaction is not a cross-shard write transaction.

db.collection.updateMany() with upsert: true can be run on an existing collection or a non-existing collection. If run on a non-existing collection, the operation creates the collection.

Tip

Oplog Entries

If a db.collection.updateMany() operation successfully updates one or more documents, the operation adds an entry on the oplog (operations log) for each updated document. If the operation fails or does not find any documents to update, the operation does not add an entry on the oplog.

Examples

Update Multiple Documents

The restaurant collection contains the following documents:

{ "_id" : 1, "name" : "Central Perk Cafe", "violations" : 3 }
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "violations" : 2 }
{ "_id" : 3, "name" : "Empire State Sub", "violations" : 5 }
{ "_id" : 4, "name" : "Pizza Rat's Pizzaria", "violations" : 8 }

The following operation updates all documents where violations are greater than 4 and $set a flag for review:

try {
   db.restaurant.updateMany(
      { violations: { $gt: 4 } },
      { $set: { "Review" : true } }
   );
} catch (e) {
   print(e);
}

The operation returns:

{ "acknowledged" : true, "matchedCount" : 2, "modifiedCount" : 2 }

The collection now contains the following documents:

{ "_id" : 1, "name" : "Central Perk Cafe", "violations" : 3 }
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "violations" : 2 }
{ "_id" : 3, "name" : "Empire State Sub", "violations" : 5, "Review" : true }
{ "_id" : 4, "name" : "Pizza Rat's Pizzaria", "violations" : 8, "Review" : true }

If no matches were found, the operation instead returns:

{ "acknowledged" : true, "matchedCount" : 0, "modifiedCount" : 0 }

Setting upsert: true would insert a document if no match was found.

Update with Aggregation Pipeline

The db.collection.updateMany() can use an aggregation pipeline for the update. The pipeline can consist of the following stages:

$addFields and its alias $set
$project and its alias $unset
$replaceRoot and its alias $replaceWith.

Example 1: Update with Aggregation Pipeline Using Existing Fields

The following examples uses the aggregation pipeline to modify a field using the values of the other fields in the document.

Create a students collection with the following documents:

db.students.insertMany( [
   { "_id" : 1, "student" : "Skye", "points" : 75, "commentsSemester1" : "great at math", "commentsSemester2" : "loses temper", "lastUpdate" : ISODate("2019-01-01T00:00:00Z") },
   { "_id" : 2, "students" : "Elizabeth", "points" : 60, "commentsSemester1" : "well behaved", "commentsSemester2" : "needs improvement", "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }
] )

Assume that instead of separate commentsSemester1 and commentsSemester2 fields, you want to gather these into a new comments field. The following update operation uses an aggregation pipeline to:

add the new comments field and set the lastUpdate field.
remove the commentsSemester1 and commentsSemester2 fields for all documents in the collection.

db.students.updateMany(
   { },
   [
      { $set: { comments: [ "$commentsSemester1", "$commentsSemester2" ], lastUpdate: "$$NOW" } },
      { $unset: [ "commentsSemester1", "commentsSemester2" ] }
   ]
)

Note

For more information on the update operators, see $set and $unset.

First Stage

The $set stage:

creates a new array field comments whose elements are the current content of the commentsSemester1 and commentsSemester2 fields and
sets the field lastUpdate to the value of the aggregation variable NOW. The aggregation variable NOW resolves to the current datetime value and remains the same throughout the pipeline. To access aggregation variables, prefix the variable with double dollar signs $$ and enclose in quotes.

Second Stage

The $unset stage removes the commentsSemester1 and commentsSemester2 fields.

After the command, the collection contains the following documents:

{ "_id" : 1, "student" : "Skye", "status" : "Modified", "points" : 75, "lastUpdate" : ISODate("2020-01-23T05:11:45.784Z"), "comments" : [ "great at math", "loses temper" ] }
{ "_id" : 2, "student" : "Elizabeth", "status" : "Modified", "points" : 60, "lastUpdate" : ISODate("2020-01-23T05:11:45.784Z"), "comments" : [ "well behaved", "needs improvement" ] }

Example 2: Update with Aggregation Pipeline Using Existing Fields Conditionally

The aggregation pipeline allows the update to perform conditional updates based on the current field values as well as use current field values to calculate a separate field value.

For example, create a students3 collection with the following documents:

db.students3.insertMany( [
   { "_id" : 1, "tests" : [ 95, 92, 90 ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") },
   { "_id" : 2, "tests" : [ 94, 88, 90 ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") },
   { "_id" : 3, "tests" : [ 70, 75, 82 ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }
] )

Using an aggregation pipeline, you can update the documents with the calculated grade average and letter grade.

db.students3.updateMany(
   { },
   [
     { $set: { average : { $trunc: [ { $avg: "$tests" }, 0 ] } , lastUpdate: "$$NOW" } },
     { $set: { grade: { $switch: {
                           branches: [
                               { case: { $gte: [ "$average", 90 ] }, then: "A" },
                               { case: { $gte: [ "$average", 80 ] }, then: "B" },
                               { case: { $gte: [ "$average", 70 ] }, then: "C" },
                               { case: { $gte: [ "$average", 60 ] }, then: "D" }
                           ],
                           default: "F"
     } } } }
   ]
)

Note

For more information on the update operators, see $set and $unset.

First Stage

The $set stage:

calculates a new field average based on the average of the tests field. See $avg for more information on the $avg aggregation operator and $trunc for more information on the $trunc truncate aggregation operator.
sets the field lastUpdate to the value of the aggregation variable NOW. The aggregation variable NOW resolves to the current datetime value and remains the same throughout the pipeline. To access aggregation variables, prefix the variable with double dollar signs $$ and enclose in quotes.

Second Stage

The $set stage calculates a new field grade based on the average field calculated in the previous stage. See $switch for more information on the $switch aggregation operator.

After the command, the collection contains the following documents:

{ "_id" : 1, "tests" : [ 95, 92, 90 ], "lastUpdate" : ISODate("2020-01-24T17:31:01.670Z"), "average" : 92, "grade" : "A" }
{ "_id" : 2, "tests" : [ 94, 88, 90 ], "lastUpdate" : ISODate("2020-01-24T17:31:01.670Z"), "average" : 90, "grade" : "A" }
{ "_id" : 3, "tests" : [ 70, 75, 82 ], "lastUpdate" : ISODate("2020-01-24T17:31:01.670Z"), "average" : 75, "grade" : "C" }

Tip

Update Multiple Documents with Upsert

The inspectors collection contains the following documents:

{ "_id" : 92412, "inspector" : "F. Drebin", "Sector" : 1, "Patrolling" : true },
{ "_id" : 92413, "inspector" : "J. Clouseau", "Sector" : 2, "Patrolling" : false },
{ "_id" : 92414, "inspector" : "J. Clouseau", "Sector" : 3, "Patrolling" : true },
{ "_id" : 92415, "inspector" : "R. Coltrane", "Sector" : 3, "Patrolling" : false }

The following operation updates all documents with Sector greater than 4 and inspector equal to "R. Coltrane":

try {
   db.inspectors.updateMany(
      { "Sector" : { $gt : 4 }, "inspector" : "R. Coltrane" },
      { $set: { "Patrolling" : false } },
      { upsert: true }
   );
} catch (e) {
   print(e);
}

The operation returns:

{
   "acknowledged" : true,
   "matchedCount" : 0,
   "modifiedCount" : 0,
   "upsertedId" : ObjectId("56fc5dcb39ee682bdc609b02"),
   "upsertedCount": 1
}

The collection now contains the following documents:

{ "_id" : 92412, "inspector" : "F. Drebin", "Sector" : 1, "Patrolling" : true },
{ "_id" : 92413, "inspector" : "J. Clouseau", "Sector" : 2, "Patrolling" : false },
{ "_id" : 92414, "inspector" : "J. Clouseau", "Sector" : 3, "Patrolling" : true },
{ "_id" : 92415, "inspector" : "R. Coltrane", "Sector" : 3, "Patrolling" : false },
{ "_id" : ObjectId("56fc5dcb39ee682bdc609b02"), "inspector" : "R. Coltrane", "Patrolling" : false }

Since no documents matched the filter, and upsert was true, updateMany() inserted the document with a generated _id, the equality conditions from the filter, and the update modifiers.

Update with Write Concern

Given a three member replica set, the following operation specifies a w of majority and wtimeout of 100:

try {
   db.restaurant.updateMany(
       { "name" : "Pizza Rat's Pizzaria" },
       { $inc: { "violations" : 3}, $set: { "Closed" : true } },
       { w: "majority", wtimeout: 100 }
   );
} catch (e) {
   print(e);
}

If the acknowledgment takes longer than the wtimeout limit, the following exception is thrown:

WriteConcernError({
   "code" : 64,
   "errmsg" : "waiting for replication timed out",
   "errInfo" : {
     "wtimeout" : true,
     "writeConcern" : {
       "w" : "majority",
       "wtimeout" : 100,
       "provenance" : "getLastErrorDefaults"
     }
   }
})

The following table explains the possible values of errInfo.writeConcern.provenance:

Provenance	Description
`clientSupplied`	The write concern was specified in the application.
`customDefault`	The write concern originated from a custom defined default value. See `setDefaultRWConcern`.
`getLastErrorDefaults`	The write concern originated from the replica set's `settings.getLastErrorDefaults` field.
`implicitDefault`	The write concern originated from the server in absence of all other write concern specifications.

Specify Collation

Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks.

A collection myColl has the following documents:

{ _id: 1, category: "café", status: "A" }
{ _id: 2, category: "cafe", status: "a" }
{ _id: 3, category: "cafE", status: "a" }

The following operation includes the collation option:

db.myColl.updateMany(
   { category: "cafe" },
   { $set: { status: "Updated" } },
   { collation: { locale: "fr", strength: 1 } }
);

Specify `arrayFilters` for an Array Update Operations

When updating an array field, you can specify arrayFilters that determine which array elements to update.

Update Elements Match `arrayFilters` Criteria

Create a collection students with the following documents:

db.students.insertMany( [
   { "_id" : 1, "grades" : [ 95, 92, 90 ] },
   { "_id" : 2, "grades" : [ 98, 100, 102 ] },
   { "_id" : 3, "grades" : [ 95, 110, 100 ] }
] )

To update all elements that are greater than or equal to 100 in the grades array, use the filtered positional operator $[<identifier>] with the arrayFilters option:

db.students.updateMany(
   { grades: { $gte: 100 } },
   { $set: { "grades.$[element]" : 100 } },
   { arrayFilters: [ { "element": { $gte: 100 } } ] }
)

After the operation, the collection contains the following documents:

{ "_id" : 1, "grades" : [ 95, 92, 90 ] }
{ "_id" : 2, "grades" : [ 98, 100, 100 ] }
{ "_id" : 3, "grades" : [ 95, 100, 100 ] }

Update Specific Elements of an Array of Documents

Create a collection students2 with the following documents:

db.students2.insertMany( [
   {
      "_id" : 1,
      "grades" : [
         { "grade" : 80, "mean" : 75, "std" : 6 },
         { "grade" : 85, "mean" : 90, "std" : 4 },
         { "grade" : 85, "mean" : 85, "std" : 6 }
      ]
   },
   {
      "_id" : 2,
      "grades" : [
         { "grade" : 90, "mean" : 75, "std" : 6 },
         { "grade" : 87, "mean" : 90, "std" : 3 },
         { "grade" : 85, "mean" : 85, "std" : 4 }
      ]
   }
] )

To modify the value of the mean field for all elements in the grades array where the grade is greater than or equal to 85, use the filtered positional operator $[<identifier>] with the arrayFilters:

db.students2.updateMany(
   { },
   { $set: { "grades.$[elem].mean" : 100 } },
   { arrayFilters: [ { "elem.grade": { $gte: 85 } } ] }
)

After the operation, the collection has the following documents:

{
   "_id" : 1,
   "grades" : [
      { "grade" : 80, "mean" : 75, "std" : 6 },
      { "grade" : 85, "mean" : 100, "std" : 4 },
      { "grade" : 85, "mean" : 100, "std" : 6 }
   ]
}
{
   "_id" : 2,
   "grades" : [
      { "grade" : 90, "mean" : 100, "std" : 6 },
      { "grade" : 87, "mean" : 100, "std" : 3 },
      { "grade" : 85, "mean" : 100, "std" : 4 }
   ]
}

Specify `hint` for Update Operations

Create a sample students collection with the following documents:

db.students.insertMany( [
   { "_id" : 1, "student" : "Richard", "grade" : "F", "points" :  0,  "comments1" : null, "comments2" : null },
   { "_id" : 2, "student" : "Jane", "grade" : "A", "points" : 60,  "comments1" : "well behaved", "comments2" : "fantastic student" },
   { "_id" : 3, "student" : "Ronan", "grade" : "F", "points" :  0,  "comments1" : null, "comments2" : null },
   { "_id" : 4, "student" : "Noah", "grade" : "D", "points" : 20,  "comments1" : "needs improvement", "comments2" : null },
   { "_id" : 5, "student" : "Adam", "grade" : "F", "points" :  0,  "comments1" : null, "comments2" : null },
   { "_id" : 6, "student" : "Henry", "grade" : "A", "points" : 86,  "comments1" : "fantastic student", "comments2" : "well behaved" }
] )

Create the following indexes on the collection:

db.students.createIndex( { grade: 1 } )

The following update operation explicitly hints to use the index { grade: 1 }:

Note

If you specify an index that does not exist, the operation errors.

db.students.updateMany(
   { "points": { $lte: 20 }, "grade": "F" },
   { $set: { "comments1": "failed class" } },
   { hint: { grade: 1 } }
)

The update command returns the following:

{ "acknowledged" : true, "matchedCount" : 3, "modifiedCount" : 3 }

To see if the hinted index is used, run the $indexStats pipeline:

db.students.aggregate( [ { $indexStats: { } }, { $sort: { name: 1 } }, { $match: {key: { grade: 1 } } } ] )

Back

db.collection.update

db.collection.updateOne

Definition

Compatibility

Note

Syntax

Parameters

Returns

Access Control

Behavior

Upsert

Update with an Update Operator Expressions Document

Update with an Aggregation Pipeline

Note

Capped Collections

Time Series Collections

Sharded Collections

Explainability

Transactions

Important

Upsert within Transactions

Tip

See also:

Write Concerns and Transactions

Oplog Entries

Examples

Update Multiple Documents

Update with Aggregation Pipeline

Example 1: Update with Aggregation Pipeline Using Existing Fields

Note

Example 2: Update with Aggregation Pipeline Using Existing Fields Conditionally

Note

Tip

See also:

Update Multiple Documents with Upsert

Update with Write Concern

Specify Collation

Specify arrayFilters for an Array Update Operations

Update Elements Match arrayFilters Criteria

Update Specific Elements of an Array of Documents

Specify hint for Update Operations

Note

Specify `arrayFilters` for an Array Update Operations

Update Elements Match `arrayFilters` Criteria

Specify `hint` for Update Operations